Artificial Intelligence and Machine Learning for Drug Discovery and Global Health
In this seminar, the Ersilia Open Source Initiative’s Dr. Gemma Turon and Dr. Miquel Duran-Frigola introduce the benefits of Ersilia’s platform for Artificial Intelligence (AI) and Machine Learning (ML) models. They hope to identify potential collaborations with NCI.
Key assets of Ersilia:
- It’s a free, online platform where you can browse a catalogue of AI/ML models.
- It can run online predictions without the need to write code.
- Research groups in underserved settings have helped the initiative develop models.
Ersilia is a non-profit organization committed to strengthening the ability of low- and middle-income countries to conduct research. By increasing access to AI and ML tools for drug discovery, Ersilia seeks to lower the cost and number of laboratory experiments.
The Data Science Seminar Series presents talks from innovators in the cancer research and informatics communities both within and outside of NCI.
Dr. Turon is a trained molecular biologist and holds a Ph.D. in colorectal cancer and stem cells. She is a co-founder of the Ersilia Open Source Initiative. Her interest lies in global health and neglected diseases, and the existing barriers to tackling urgent health issues in developing countries.
Dr. Duran-Frigola’s research interests lie at the intersection of drug discovery and large-scale biological data analysis. He is a co-founder and lead scientist at the Ersilia Open Source Initiative, and he hopes to apply his data science skills in underfunded settings, including research institutes in low- and middle-income countries.
Upcoming Events
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2024 Children’s Brain Tumor Network (CBTN) SummitOctober 09, 2024 - October 11, 2024The Role of MIDRC in Medical Imaging AIOctober 11, 2024Rationalized Patient Match and Clinical Trial Design with Large/Moderate Language Models on EHR DataOctober 15, 2024NCI Office of Data Sharing’s Annual Data Sharing Symposium: Driving Cancer Advances Through Impactful ResearchOctober 16, 2024Generative AI for Modeling Single-cell State and ResponseOctober 16, 2024